Deep Learning – what on earth does that mean?
Have you ever wondered just what this phrase Deep Learning is referring to and why it matters? If so then this post is for you!
In my last post, I demystified a variety of buzzwords, and explained that Deep Learning is a subset of Machine Learning. This post explores the world of deep learning for non-mathematicians (just like myself). In doing so it:
- Touches on convolutional neural networks;
- Explains the impact Deep Learning is having on Cognitive Computing;
- Outlines a few examples of Cognitive Computing (Deep Learning) in action.
Starting with Artificial Neural Networks
To understand Deep Learning, you must first understand a little about Artificial Neural Networks. Don’t worry – as I am not a data scientists I will not try to describe the mathematics behind it all. That means no talk beyond this sentence of weighting, activation functions and more.
Deep Learning normally revolves around the use of Artificial Neural Networks with more than 1 hidden layers. More on what a hidden layer is shortly. The theory is that the more hidden layers you have the more you can isolate specific regions of data to classify things.
Continue reading Deep Learning – What is it and why does it matter?
Artificial Intelligence (AI) – Demystifying the latest buzzword
Everywhere you turn today someone is talking about Artificial Intelligence (AI). It appears to have taken over as the largest buzzword since Big Data.
Progressive organizations are actively seeking ways to apply AI. They want to use it to advance their businesses and build new experiences for those they interact with internally and externally.
Alas there is great confusion as to what AI means. That just gets worse when people mix that up with terms such as Machine Learning and Deep Learning. If you ask several different people their view on what is AI, or Machine Learning, you will get several different answers.
It is the age-old problem of describing an elephant dependent on which side of it you touch while blindfolded.
AI is not a new topic. People have been pursuing AI since the 1940s. Machine Learning, which has developed from the field of Artificial Intelligence, has been around since at least the 1980s and Deep Learning, which is a subset of Machine Learning, has been rapidly gaining in popularity over the past 10 years. This post explores all these topics setting the scene for some upcoming posts.
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Exponential Times – Will Your Organization Adapt or Expire!
Digital transformation is either the worlds largest hoax or it is the most important single business modifier of our times. My opinion is that is no hoax. We live in exponential times and business model innovation tied to an ever increasing digital world is vital for organizations to adapt, survive and thrive.
Exponential Times refers to the fact that burying your head in the sand, while digital transformation occurs around you, and doing nothing is harmless until it is too late to react to sudden shifts in which case you face a fight to survive let alone thrive.
When I first show this image to people they think I am referring to the pace of technology change. No doubt technology is changing dramatically around us. What I am referring to though is the rate of business model change being enabled by rapidly shifting technology. For me digital transformation does not happen without business model innovation.
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Mixing Electricity and Water
Normally you should not mix water and electricity unless you want a shocking experience.
When it comes to how the use of data and analytics can transform water and electricity utilities into intelligent enterprises we can make an exception.
In this post I discuss the challenges utilities are facing over the upcoming years and lay out why Analytics and IoT is going to be increasingly important. At the end I include a link to an on-demand Webinar with an organization who has already delivered solutions in this space: Saviant Consulting.
Continue reading Transforming Utilities with IoT and Analytics
Nothing New for Agribusiness
Innovation in the agribusiness is not a new phenomenon. It has been going on for many years often without many people being aware of it outside that business itself. That continuous innovation has resulted in todays agribusinesses being much more productive than previous generations of agribusinesses.
Some studies put the average agribusinesses ability to produce at 262% better than it was as recently as 1950. This has been achieved with 2% fewer inputs than before where inputs are things like labor, seeds, feed and fertilizer.
To that end it is clear the agribusiness has come a long way from where it started in the 18th century. At that time it was driven by Oxen and horses for power, crude wooden plows and lots of work done by hand to sow seeds and cultivate crops.
If we fast forward through the many innovations, triggered by the industrial revolution (iron ploughs, cotton gin, tractors etc), we eventually arrive to the point where information technology started to be used extensively in the in the late 1980’s and early 1990’s quickly followed by the use of satellites to plan work in the late 1990’s.
To the outside the agribusiness looks like a slow moving industry when it fact it is clear to those closer that it is moving at an astonishing pace!
Continue reading Transforming the Agribusiness with IoT and Analytics